Outline of weeks 7-13

(this document will be updated some as the weeks proceed)

• Also see guide to Alex Holcombe and his lectures, posted on Canvas

Week 7: Stats and studies: Correlation and causation

• Correlation and causation intro
  • Learning objectives:
    • Understand and apply three causal models to explaining correlations
    • Know the term “spurious correlation”
• Where does data come from?
• Understanding correlation more deeply
  • Correlation of X with Y same as Y with X
  • Measurement and correlation
    • Correlation not affected by changes in units
  • When linear regression is not appropriate: COVID-19 interactive lesson
• Three causal models
  • X causes Y, Y causes X, a third variable causes both
  • The third-cause fallacy
• A causal model can be at any level of detail

• Explanatory, Outcome, and Nuisance variables
  • IV (an explanatory variable in an experiment) and DV (an outcome variable in an experiment). Bruce slide:
  • Nuisance variable
    • Is not different, on average, for the different levels of the variable we are interested in
  • Confound variables
    • Is different for the different levels of the variable we are interested in.
    • Bruce slide:
  • Can you distinguish between confounds and nuisance variables?
  • Random assignment
    • Bruce slide:
• Randomisation and control group
  • Bruce slide
• Time as a third variable
  • Relationship between many observed variables, such as pirates and high average temperature, is confounded by time.
  • Time: Post hoc ergo propter hoc (After this therefore because of this) fallacy

Week 8

• Dichotomous correlation
  • dichotomous variables and correlation
  • Contingency tables
  • Later you will see the link to arguments and logic
• 4 kinds of control
  • Statistical adjustment - controlling for confounds
  • A controlled variable - matching
• Causal phrases verus correlational phrases, see “Distinguish correlational and causal..” video on Canvas

• Recognizing observational studies in the news

• “Controlling for” in regression and multicollinearity

• Epidemiology

  • Smoking has a big effect,
  • Nutrition effects are small
  • Many false positives, which we’ll explain later

Week 9

• Hypothesis testing review and extension
  • Review of hypothesis testing (recall tutorial week 5 and Bruce’s 18 March lecture)
  • Bruce slide:
  • False positives, false negatives, true positives, true negatives
  • Hypothesis testing and medical testing
    • Sensitivity and specificity
• Why does the news have lots of false positives?
  • Excess of statistical comparisons
  • More reasons for errors in science given later in the class, after logic and arguments

Arguments and logic, Weeks 9-10

We’re going to go back and forth between bare-bones examples and arguments from the wild, giving you more and more tools to deal with the real-world ones.

A 9 min intro video on youtube, but it uses different terminology than we use in this unit.

• We need to know good reasons for believing things. Ideally,
  • Assumptions are true
  • Logic must be airtight (inescapable)
• Syllogisms introduction
  • Suppositionally inescapable; inescapable
• Truth contingency tables
• Necessary and sufficient
• Syllogisms
  • Suppositionally inescapable, inescapable, suppositionally solid, solid
• Towards real-world arguments
• The vegetarianism argument, student responses, and the difficulty of decoupling
• Redundant premises
• Implicit premises

Arguments and reasoning in the wild, Weeks 11-13

Week 11

• Poly-syllogism

• Casting an argument

• Mindset for real-world (4 slides)

  • I will happily change, for I seek the truth - Aurelius.
  • Most arguments are people just trying to win - to get what they want. Persuasion.
  • But ideally you don’t just want to win, you actually want to learn and find things out.

Fallacies.

• Loose language in the wild

  • hyperbole
  • principle of charity (related to steel man, discussed later)

• Casting the we must bomb Iran argument

• Induction and deduction

  • Process of elimination, Sherlock Holmes
  • Abduction

Weeks 12 and 13

• Deduction and induction in science
  • reading
• Analysing scientific abstracts

• Analysing the Molly and Bea argument
• Hawthorne and Expectancy effects
  • Experimenter
  • Participant
  • Addressing expectancy effects: blinding
• Rhetoric, mere rhetoric, and informal fallacies
• Boosters and hedgers
• Post hoc ergo propter hoc, again
  • Video combining this fallacy with deductive and inductive logic. Good way to bring previous bits of the class together!
• Ad populum
• Ad hominem “To the person”:
  • Puerile name-calling
  • Vaccilation
  • Appeal to authority
    • Appropriate versus inappropriate
• Straw man
  • Relation to principle of charity
• Fallacy of the single cause
• Circularity
• Minimising evidence against
  • Overwhelming exception
  • No True Scotsman
• Slippery slope